基于VMD-IGWO-SVM的风电功率超短期预测研究

Research on Ultra-Short-Term Wind Power Prediction Based on VMD-IGWO-SVM

为了提高风电功率预测精度,保证风能的有效利用,提出一种基于变分模态分解和改进灰狼算法优化支持向量机的风电功率超短期组合预测模型。采用变分模态分解将风电功率序列分解为一系列具有不同中心频率的模态分量以降低其随机性,将各分量分别建立支持向量机预测模型,并采用改进灰狼算法对其参数寻优,将各分量的预测值叠加重构得到最终的预测值。实例仿真表明,所提的组合预测模型与其他预测模型相比具有更高的预测精度。

In order to improve the accuracy of wind power prediction and to ensure the effective utilization of wind energy,this paper proposed a combined model based on VMD and SVM optimized by IGWO for ultra-short-term wind power prediction.VMD was used to decompose the wind power series into a series of modal components with different central frequencies to reduce its randomness.The SVM prediction model was established for each component and its parameters were optimized by IGWO.The predicted value of each component was superimposed to get the final predicted value.Simulation results show that compared with other prediction models,the proposed combination prediction model has higher prediction accuracy.